llm-pipeline-combines-three-ml-paradigms

OUT derived (depth 1)

Created 2026-06-21T10:06:02+00:00 · Reviewed 2026-06-21T11:03:09+00:00

The modern LLM training pipeline synthesizes all three classical ML paradigms in sequence: self-supervised pretraining (unsupervised), instruction fine-tuning (supervised), and RLHF alignment (reinforcement learning).

Justifications

SL — LLM pipeline stages map one-to-one onto the three classical ML paradigms

Antecedents (all must be IN):

  • IN llm-training-pipeline-stages — Modern LLM training pipeline built on transformers: self-supervised learning → fine-tuning → instruction tuning → RLHF/Constitutional AI
  • IN ml-three-classical-paradigms — The three classical machine learning paradigms are supervised learning (labelled data), unsupervised learning (no labels), and reinforcement learning (reward signal)
  • IN rlhf-connects-base-lm-to-aligned-chatbots — RLHF (Reinforcement Learning from Human Feedback) is the key technique connecting base language models to aligned, deployable chatbots.